Abstract
Classification is an important issue in data mining and knowledge discovery, and the attribute reduction has been proven to be effective in improving the classification accuracy in many applications. In this paper, we first apply rough set theory to reduce irrelative attribute and retain the important attributes, and the input neuron based on the important attributes can simplify the structure of BP-neuron network and improve classification accuracy. Then an efficient BP-neural network classification model based on attribute reduction is developed for high-dimensional data analysis. Finally, the experimental results demonstrate the efficiency and effectiveness of the proposed model.
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Wang, Y., Zheng, X. (2014). An Efficient BP-Neural Network Classification Model Based on Attribute Reduction. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds) Rough Sets and Knowledge Technology. RSKT 2014. Lecture Notes in Computer Science(), vol 8818. Springer, Cham. https://doi.org/10.1007/978-3-319-11740-9_20
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DOI: https://doi.org/10.1007/978-3-319-11740-9_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11739-3
Online ISBN: 978-3-319-11740-9
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